We introduce Noisy Feature Mixup, an effective data augmentation method that combines mixup and noise injection. We identify the implicit regularization effects of NFM, showing that the effects are amplifications of those of manifold mixup and noise injection. Moreover, we demonstrate the benefits of NFM in terms of superior model robustness, both theoretically and experimentally. Our work inspires a range of interesting future directions, including theoretical investigations of the trade-offs between accuracy and robustness for NFM and applications of NFM beyond computer vision tasks.
Further, it will be interesting to study whether NFM may also lead to better model calibration by extending the analysis of [1]}, [2]}.
